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Die Prüfung von Google Associate-Cloud-Engineer (Google Associate Cloud Engineer) ist eine Zertifizierungsprüfung, mit der die Kenntnisse einer Person im Bereich Cloud Computing getestet werden sollen. Diese Prüfung richtet sich an diejenigen, die über Kenntnisse und Erfahrung in der Google Cloud -Plattform und verwandten Technologien verfügen. Personen, die diese Prüfung bestehen, werden als Google Associate Cloud Engineers anerkannt. Dies ist eine wertvolle Zertifizierung für Personen, die eine Karriere im Cloud -Computing oder IT suchen.
>> Associate-Cloud-Engineer Prüfung <<
Wenn Sie Fast2test wählen, steht der Erfolg schon vor der Tür. Und bald können Sie Google Associate-Cloud-Engineer Zertifikat bekommen. Das Produkt von Fast2test bietet Ihnen 100%-Pass-Garantie und auch einen kostenlosen einjährigen Update-Service.
Der erfolgreiche Abschluss der Google Associate-Cloud-Engineer-Prüfung zeigt, dass eine Person über das Wissen und die Fähigkeiten verfügt, um Anwendungen auf GCP zu implementieren und zu verwalten. Die Prüfung umfasst eine Vielzahl von Themen, einschließlich Erstellung von GCP-Projekten, Bereitstellung von Anwendungen, Netzwerken, Speicherung, Computing und Sicherheit. Kandidaten, die die Prüfung bestehen, können ihre Kompetenz bei der Verwendung von GCP-Diensten zur Gestaltung und Bereitstellung von hochverfügbaren, skalierbaren und sicheren Anwendungen nachweisen. Diese Zertifizierung wird in der Branche sehr geschätzt und kann für Einzelpersonen, die ihre Karriere in der Cloud-Computing-Branche vorantreiben möchten, neue Jobmöglichkeiten eröffnen.
143. Frage
You are managing several Google Cloud Platform (GCP) projects and need access to all logs for the past 60 days. You want to be able to explore and quickly analyze the log contents. You want to follow Google- recommended practices to obtain the combined logs for all projects. What should you do?
Antwort: A
Begründung:
Explanation
Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right.
Log entries are held in Stackdriver Logging for a limited time known as the retention period which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks.
Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right.
While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.Ref: https://cloud.google.com/logging/docs/export/configure_export_v2 Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.Ref: https://cloud.google.com/logging/docs/export/configure_export_v2 Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage.
Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.Ref: https://cloud.google.com/logging/docs/export/configure_export_v2 Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage.
Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.Ref: https://cloud.google.com/bigquery/docs/best-practices-storage
144. Frage
You have created an application that is packaged into a Docker image. You want to deploy the Docker image as a workload on Google Kubernetes Engine. What should you do?
Antwort: C
Begründung:
Upload your docker image on container registry then give a ref while creating deployment.
145. Frage
You have a web application deployed as a managed instance group. You have a new version of the application to gradually deploy. Your web application is currently receiving live web traffic.
You want to ensure that the available capacity does not decrease during the deployment. What should you do?
Antwort: C
Begründung:
We need to ensure the global capacity remains intact, for that reason we need to establish maxUnavailable to 0. On the other hand, we need to ensure new instances can be created. We do that by establishing the maxSurge to 1.
Option C is more expensive and more difficult to set up and option D won't meet requirements since it won't keep global capacity intact.
https://cloud.google.com/compute/docs/instance-groups/rolling-out-updates-to-managed- instance-groups#options
146. Frage
You are planning to migrate your on-premises VMs to Google Cloud. You need to set up a landing zone in Google Cloud before migrating the VMs. You must ensure that all VMs in your production environment can communicate with each other through private IP addresses. You need to allow all VMs in your Google Cloud organization to accept connections on specific TCP ports. You want to follow Google-recommended practices, and you need to minimize your operational costs. What should you do?
Antwort: A
Begründung:
The goal is to create a landing zone facilitating private IP communication across production projects and apply organization-wide firewall rules, following best practices and minimizing operational costs.
Network Structure:Individual VPCs with Peering (A, B): While VPC Peering allows private connectivity, managing a full mesh or complex peering topology across many projects becomes operationally complex and can hit peering limits. It's not the recommended pattern for centralized connectivity in a landing zone.
Shared VPC (C, D): This is the Google-recommended practice for scenarios where resources from multiple projects need to communicate privately within a common VPC network. A central host project owns the network, and service projects use it. This simplifies network administration and connectivity.
Firewall Rules:Organization Policies (A, C): These enforce organizational constraints (e.g., disable external IPs, restrict locations) but do not define specific network firewall rules (like allowing TCP ports).
Hierarchical Firewall Policies (B, D): These allow defining firewall rules at the Organization or Folder level, which are inherited by resources in descendant projects/folders. This is the mechanism to apply consistent firewall rules (like allowing specific TCP ports) across all VMs in the organization (or a specific folder) efficiently, without managing rules in each individual VPC or project.
Combining Shared VPC for the network structure (best practice for cross-project private communication and central management) with Hierarchical Firewall Policies (for applying organization-wide firewall rules) meets all requirements efficiently and follows Google recommendations.
References:
Shared VPC Overview: "Shared VPC allows an organization to connect resources from multiple projects to a common Virtual Private Cloud (VPC) network..." - https://cloud.google.com/vpc/docs/shared-vpc Hierarchical firewall policies: "Hierarchical firewall policies let you create and enforce a consistent firewall policy across your organization... They can be configured to explicitly deny traffic, or allow traffic..." -
https://cloud.google.com/firewall/docs/hierarchical-firewall-policies
Google Cloud security foundations guide: Often recommends Shared VPC and centralized firewall management (using Hierarchical Firewalls or traditional firewalls with tags in the host project) as part of a secure landing zone. - (Conceptual reference, specific document may vary)
147. Frage
You are configuring service accounts for an application that spans multiple projects. Virtual machines (VMs) running in the web-applications project need access to BigQuery datasets in crm-databases-proj. You want to follow Google-recommended practices to give access to the service account in the web-applications project. What should you do?
Antwort: C
148. Frage
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